2,480 research outputs found
Cooperative Caching and Transmission Design in Cluster-Centric Small Cell Networks
Wireless content caching in small cell networks (SCNs) has recently been
considered as an efficient way to reduce the traffic and the energy consumption
of the backhaul in emerging heterogeneous cellular networks (HetNets). In this
paper, we consider a cluster-centric SCN with combined design of cooperative
caching and transmission policy. Small base stations (SBSs) are grouped into
disjoint clusters, in which in-cluster cache space is utilized as an entity. We
propose a combined caching scheme where part of the available cache space is
reserved for caching the most popular content in every SBS, while the remaining
is used for cooperatively caching different partitions of the less popular
content in different SBSs, as a means to increase local content diversity.
Depending on the availability and placement of the requested content,
coordinated multipoint (CoMP) technique with either joint transmission (JT) or
parallel transmission (PT) is used to deliver content to the served user. Using
Poisson point process (PPP) for the SBS location distribution and a hexagonal
grid model for the clusters, we provide analytical results on the successful
content delivery probability of both transmission schemes for a user located at
the cluster center. Our analysis shows an inherent tradeoff between
transmission diversity and content diversity in our combined
caching-transmission design. We also study optimal cache space assignment for
two objective functions: maximization of the cache service performance and the
energy efficiency. Simulation results show that the proposed scheme achieves
performance gain by leveraging cache-level and signal-level cooperation and
adapting to the network environment and user QoS requirements.Comment: 13 pages, 10 figures, submitted for possible journal publicatio
Content Placement in Cache-Enabled Sub-6 GHz and Millimeter-Wave Multi-antenna Dense Small Cell Networks
This paper studies the performance of cache-enabled dense small cell networks
consisting of multi-antenna sub-6 GHz and millimeter-wave base stations.
Different from the existing works which only consider a single antenna at each
base station, the optimal content placement is unknown when the base stations
have multiple antennas. We first derive the successful content delivery
probability by accounting for the key channel features at sub-6 GHz and mmWave
frequencies. The maximization of the successful content delivery probability is
a challenging problem. To tackle it, we first propose a constrained
cross-entropy algorithm which achieves the near-optimal solution with moderate
complexity. We then develop another simple yet effective heuristic
probabilistic content placement scheme, termed two-stair algorithm, which
strikes a balance between caching the most popular contents and achieving
content diversity. Numerical results demonstrate the superior performance of
the constrained cross-entropy method and that the two-stair algorithm yields
significantly better performance than only caching the most popular contents.
The comparisons between the sub-6 GHz and mmWave systems reveal an interesting
tradeoff between caching capacity and density for the mmWave system to achieve
similar performance as the sub-6 GHz system.Comment: 14 pages; Accepted to appear in IEEE Transactions on Wireless
Communication
Joint and Competitive Caching Designs in Large-Scale Multi-Tier Wireless Multicasting Networks
Caching and multicasting are two promising methods to support massive content
delivery in multi-tier wireless networks. In this paper, we consider a random
caching and multicasting scheme with caching distributions in the two tiers as
design parameters, to achieve efficient content dissemination in a two-tier
large-scale cache-enabled wireless multicasting network. First, we derive
tractable expressions for the successful transmission probabilities in the
general region as well as the high SNR and high user density region,
respectively, utilizing tools from stochastic geometry. Then, for the case of a
single operator for the two tiers, we formulate the optimal joint caching
design problem to maximize the successful transmission probability in the
asymptotic region, which is nonconvex in general. By using the block successive
approximate optimization technique, we develop an iterative algorithm, which is
shown to converge to a stationary point. Next, for the case of two different
operators, one for each tier, we formulate the competitive caching design game
where each tier maximizes its successful transmission probability in the
asymptotic region. We show that the game has a unique Nash equilibrium (NE) and
develop an iterative algorithm, which is shown to converge to the NE under a
mild condition. Finally, by numerical simulations, we show that the proposed
designs achieve significant gains over existing schemes.Comment: 30 pages, 6 pages, submitted to IEEE GLOBECOM 2017 and IEEE Trans.
Commo
Mitigating Interference in Content Delivery Networks by Spatial Signal Alignment: The Approach of Shot-Noise Ratio
Multimedia content especially videos is expected to dominate data traffic in
next-generation mobile networks. Caching popular content at the network edge
has emerged to be a solution for low-latency content delivery. Compared with
the traditional wireless communication, content delivery has a key
characteristic that many signals coexisting in the air carry identical popular
content. They, however, can interfere with each other at a receiver if their
modulation-and-coding (MAC) schemes are adapted to individual channels
following the classic approach. To address this issue, we present a novel idea
of content adaptive MAC (CAMAC) where adapting MAC schemes to content ensures
that all signals carry identical content are encoded using an identical MAC
scheme, achieving spatial MAC alignment. Consequently, interference can be
harnessed as signals, to improve the reliability of wireless delivery. In the
remaining part of the paper, we focus on quantifying the gain CAMAC can bring
to a content-delivery network using a stochastic-geometry model. Specifically,
content helpers are distributed as a Poisson point process, each of which
transmits a file from a content database based on a given popularity
distribution. It is discovered that the successful content-delivery probability
is closely related to the distribution of the ratio of two independent shot
noise processes, named a shot-noise ratio. The distribution itself is an open
mathematical problem that we tackle in this work. Using stable-distribution
theory and tools from stochastic geometry, the distribution function is derived
in closed form. Extending the result in the context of content-delivery
networks with CAMAC yields the content-delivery probability in different closed
forms. In addition, the gain in the probability due to CAMAC is shown to grow
with the level of skewness in the content popularity distribution.Comment: 32 pages, to appear in IEEE Trans. on Wireless Communicatio
Cooperative Local Caching under Heterogeneous File Preferences
Local caching is an effective scheme for leveraging the memory of the mobile
terminal (MT) and short range communications to save the bandwidth usage and
reduce the download delay in the cellular communication system. Specifically,
the MTs first cache in their local memories in off-peak hours and then exchange
the requested files with each other in the vicinity during peak hours. However,
prior works largely overlook MTs' heterogeneity in file preferences and their
selfish behaviours. In this paper, we practically categorize the MTs into
different interest groups according to the MTs' preferences. Each group of MTs
aims to increase the probability of successful file discovery from the
neighbouring MTs (from the same or different groups). Hence, we define the
groups' utilities as the probability of successfully discovering the file in
the neighbouring MTs, which should be maximized by deciding the caching
strategies of different groups. By modelling MTs' mobilities as homogeneous
Poisson point processes (HPPPs), we analytically characterize MTs' utilities in
closed-form. We first consider the fully cooperative case where a centralizer
helps all groups to make caching decisions. We formulate the problem as a
weighted-sum utility maximization problem, through which the maximum utility
trade-offs of different groups are characterized. Next, we study two benchmark
cases under selfish caching, namely, partial and no cooperation, with and
without inter-group file sharing, respectively. The optimal caching
distributions for these two cases are derived. Finally, numerical examples are
presented to compare the utilities under different cases and show the
effectiveness of the fully cooperative local caching compared to the two
benchmark cases
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